import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# Plot Netflix stock
plt.plot(stocks.date.values, stocks.NFLX.values)
# Make plot on x-axis readable
plt.xticks(rotation='vertical')
plt.xticks(range(0, stocks.shape[0], 10))
# Add titles and labels
plt.title('Netflix stock')
plt.xlabel('Date')
plt.ylabel('Stock value')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# Plot more than one stock (not all)
plt.plot(stocks.date.values, stocks.GOOG.values, label = "Google", linestyle="-")
plt.plot(stocks.date.values, stocks.NFLX.values, label = "Netflix", linestyle="--")
plt.plot(stocks.date.values, stocks.AMZN.values, label = "Amazon", linestyle="-.")
plt.plot(stocks.date.values, stocks.FB.values, label = "Facebook", linestyle=":")
# Make plot on x-axis readable
plt.xticks(rotation='vertical')
plt.xticks(range(0, stocks.shape[0], 10))
# Add titles, labels and legend
plt.title('Netflix stock')
plt.xlabel('Date')
plt.ylabel('Stock value')
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
g = sns.FacetGrid(tips, col='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()
sns.jointplot(x='total_bill', y='tip', data=tips)
plt.show()
sns.scatterplot(x='total_bill', y='tip', data=tips, hue='sex').set(title='Tips male vs. female')
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
df_stocks = stocks.melt(id_vars=['date'], var_name='company')
df_stocks.head()
| date | company | value | |
|---|---|---|---|
| 0 | 2018-01-01 | GOOG | 1.000000 |
| 1 | 2018-01-08 | GOOG | 1.018172 |
| 2 | 2018-01-15 | GOOG | 1.032008 |
| 3 | 2018-01-22 | GOOG | 1.066783 |
| 4 | 2018-01-29 | GOOG | 1.008773 |
px.line(df_stocks, 'date', 'value', color='company', symbol='company')
fig = px.scatter(tips, x='total_bill', y='tip', color='sex', facet_col='smoker',
facet_row='time')
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x='pop', y=df_2007_new.index, color=df_2007_new.index,
text='pop', title="Population of different continents for the year 2007",
text_auto='.2s')
fig.update_layout(yaxis={'categoryorder':'total descending'})
fig.update_traces(textposition='outside')
fig.show()